Behrooz Mamandipoor

Behrooz Mamandipoor
Fondazione Bruno Kessler | FBK · e-Health

About

19
Publications
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89
Citations
Introduction
Behrooz Mamandipoor is a researcher in the Data Science for Health group of Fondazione Bruno Kessler (FBK) Research Institute, in Trento, Italy. He is broadly interested in designing machine learning and deep learning algorithms for applications in healthcare with the goal of improving health outcomes by optimizing treatment strategies. Currently, the focus of his research is on predictive models derived principally from the Electronic Health Records (EHR) of patients from Intensive Care Units.

Publications

Publications (19)
Article
Full-text available
Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict...
Article
Full-text available
Introduction: Intoxications are common in intensive care units (ICUs). The number of causative substances is large, mortality usually low. This retrospective cohort study aims to characterize differences of intoxicated compared to general ICU patients, point out variations according to causative agents, as well as to highlight differences between...
Preprint
A bstract Background racial bias has been shown to be present in clinical data, affecting patients unfairly based on their race, ethnicity and socio-economic status. This problem has the potential to be significantly exacerbated in the light of Artificial Intelligence-aided clinical decision making. We sought to investigate whether bias can be int...
Article
Full-text available
Background: Mortality in sepsis remains high. Studies in small cohorts have shown that red cell distribution width (RDW) is associated with mortality. The aim of this study was to validate these findings in a large multi-centre cohort. Methods: We conducted this retrospective analysis of the multi-center eICU Collaborative Research Database in 1...
Article
Background: The SARS-CoV-2 coronavirus disease (COVID-19) pandemic is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. Objective: This study aimed to evaluate machine-learning based prognostication models for critically ill elderly COVID...
Article
Full-text available
Screening for colorectal cancer (CRC) continues to rely on colonoscopy and/or fecal occult blood testing since other (non-invasive) risk-stratification systems have not yet been implemented into European guidelines. In this study, we evaluate the potential of machine learning (ML) methods to predict advanced adenomas (AAs) in 5862 individuals parti...
Article
Full-text available
Background Higher survival has been shown for overweight septic patients compared with normal or underweight patients in the past. This study aimed at investigating the management and outcome of septic ICU patients in different body mass index (BMI) categories in a large multicenter database. Methods In total, 16,612 patients of the eICU collabora...
Preprint
BACKGROUND The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk. OBJECTIVE The aim of this study was to evaluate machine learning–based prognostication models for critically ill elderly COVID-19 pat...
Article
Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care. Methods: This ana...
Preprint
Purpose. Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can...
Article
Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict...
Article
Full-text available
Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neu...
Article
Purpose : To evaluate the application of machine learning methods, specifically Deep Neural Networks (DNN) models for intensive care (ICU) mortality prediction. The aim was to predict mortality within 96 hours after admission to mirror the clinical situation of patient evaluation after an ICU trial, which consists of 24-48 hours of ICU treatment an...
Article
Purpose: Early lactate clearance is an important parameter for prognosis assessment and therapy control in sepsis. Patients with a lactate clearance >0% might differ from patients with an inferior clearance in terms of intensive care management and outcomes. This study analyzes a large collective with regards to baseline risk distribution and outc...
Article
Background : Female and male critically ill septic patients might differ with regards to risk distribution, management, and outcomes. We aimed to compare male versus female septic patients in a large collective with regards to baseline risk distribution and outcomes. Methods : In total, 17,146 patients were included in this analysis, 8781 (51%) ma...
Article
Blood lactate concentration is a reliable risk indicator of deterioration in critical care requiring frequent blood sampling. However, lactate measurement is an invasive procedure that can increase risk of infections. Yet there is no clinical consensus on the frequency of measurements. In response we investigate whether machine learning algorithms...
Article
Full-text available
Wastewater treatment plants use many sensors to control energy consumption and discharge quality. These sensors produce a vast amount of data which can be efficiently monitored by automatic systems. Consequently, several different statistical and learning methods are proposed in the literature which can automatically detect faults. While these meth...
Preprint
Full-text available
Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a...

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